The road to becoming a Data Scientist, with Helen Ørn Gjerdrum and Ashesh Raj Gnawali

Data science is a crucial part of any organization, where those in the role use their statistical and analytical skills to create influential, data-driven solutions through collecting, analyzing and interpreting data sets. The Data Scientists at Marketer play a key part in the company’s focus on creating scalable, data-driven products as they tackle complex data-related issues.

Helen Ørn Gjerdrum

Tell us about your journey to becoming a Data Scientist.

I started at Marketer as a Junior Natural Language Processing Engineer right after my Bachelor in Language Technology at UiO. Back then, I worked mainly with an NLP system that integrated features like text analysis and text generation into our automated marketing solutions. A few months back, I got the opportunity to join the data science team at Marketer. I have always wanted to try my hand at tackling real-life data related challenges as a Data Scientist, and I really love the work I do in my new role.

Are there any similarities in the two roles?

They both have similarities and differences. Both roles call for a certain way of thinking, being able to analyze and adjust, research and try new things in fields that are always evolving and where nothing is set in stone. Machine learning also plays a big role in both data science and NLP, building models that can help us predict, generate, forecast or analyze content. I actually did a lot of data science-related work during my Bachelor's degree, so I am already familiar with a lot of the libraries, tools, programming languages and methods I use in my new role, so the transition has worked out really well for me.

Why is data science important within proptech?

Data science is important because of the great value in being a data-driven proptech company in a world where so much is happening online. The insights presented by the data team give us crucial information that helps us understand the real estate market better, enabling us to more effectively deliver quality, forward-thinking services to our customers.

How is it to work in the data science team at Marketer?

There is a lot of potential in the data available, which creates an environment where it is easy to pitch new ideas to each other within the data team and across departments. We usually work on projects individually or in small teams, but there is always a feeling of collaboration as we help and support each other along the way. Additionally, it is so easy to do interdisciplinary work at Marketer because everyone is eager to help and contribute with their knowledge, ideas and solutions.

Ashesh Raj Gnawali

Tell us about your journey to becoming a Data Scientist.

I actually have a Bachelor’s degree in Electrical Engineering from Tribhuvan University in Nepal, where I was gradually introduced to data science which sparked my interest in the field. I continued my own exploration through online courses, which in the end led me to pursue a Master’s degree in Data Science at NMBU (Norwegian University of Life Sciences). I started my career at Marketer as a Data Analyst just after graduating, where I worked mainly with analyzing the marketing campaigns, looking for insights on different campaign performance metrics and translating the findings. I was also responsible for identifying and researching new data sources for several countries. In February this year, I got the opportunity to join the data science team which was perfect due to my background and passion for data science.

What do you enjoy about Data Science?

The process of creating something from scratch never ceases to amaze me, and I enjoy data science for the journey rather than the outcomes. What also stands out to me is the possibility to make data-informed decisions and the feeling that you are a part of a bigger discussion. Lastly, I really like cleaning data, which is something many data scientists may find tedious, but for me, it provides a strange sensation of fulfillment.

What is the difference between being a Data Analyst and a Data Scientist?

In short, a Data Analyst is someone who analyzes data to find patterns and insights, as well as prepares data visualizations that aid the rest of the business to get a better understanding of the data. As a Data Scientist, you do many of the same activities but it also involves data modeling to predict the future based on past patterns. For example, in Marketer, we have a property value estimator model that provides a value estimate of properties in Norway with a 5% mean absolute percentage error, which is a very good model.

Can you highlight some of the key learnings from your career so far?

Well, working in data science requires a lot of creativity and problem-solving skills, as you might not always find the data variables that you are looking for, which forces you to explore other ways to create value. So, you never stop learning in this field. Other than that, I have expanded my knowledge a great deal from my diverse career and background, especially in building ETL pipelines, data wrangling, data mining, and data visualization which are essential skills for any data personnel.